Identification of <i>Pseudomonas aeruginosa</i>-induced genes in human mast cells using suppression subtractive hybridization: up-regulation of IL-8 and CCL4 production
Bibliographic record
Abstract
Mast cells have recently been found to be a major player in the host defence against bacterial infection through secretion of potent mediators. Identification of bacteria-induced mast cell mediators and intracellular signalling molecules involved during bacterial infection remains a major area of investigation. Recently we found an active interaction between mast cells and Pseudomonas aeruginosa bacteria. To further characterize specific genes in mast cells modulated by P. aeruginosa, we used a new approach for the study of mast cell-bacteria interaction; the suppression subtractive hybridization (SSH). SSH approach does not require a prerequisite knowledge of target genes and does not rely on the availability of the assay reagents for the specific genes. Using SSH, 94 clones were randomly selected from the subtracted cDNA library for differential screening leading to the identification of 14 P. aeruginosa-up-regulated transcripts. Sequence analysis revealed that expression of IL-1, IL-8 and CCL4 was increased by human mast cells after P. aeruginosa infection. Increased production of IL-1, IL-8 and CCL4 was confirmed at the protein levels. In addition, sequence analysis of the clones also suggests that ribosomal protein S3 and cytochrome b as well as additional 4 uncharacterized genes may potentially be involved in P. aeruginosa pathogenesis. Thus, SSH is an effective approach by identifying potential molecular targets for the study of mechanisms involved in P. aeruginosa and mast cell interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".